Automatic item generation (AIG) is an effective way of constructing many items with predictable difficulties, based on a set of predefined task parameters.
Hence, the ambiguity of the target-distractor decision places a particularly high demand on frontoparietal resources at the earliest stages of the task, when the task parameters and stimulus set are novel.
Subjects performed 4 study sessions using the same task parameters and stimulus set (60 slides at each study session; half emotional half neutral) across 3 days: 1 PM day-1 (48 h prior to testing), 1 PM day-2 (24 h prior to testing), 1 PM day-3 (4 h prior to testing) and 5 PM day-3; 15 min prior to recognition testing at 5 15 PM.
Conversely, the similarity of the attended stimulus to the current target object plays a larger role in predicting the BOLD response in the later stages of the task, when the task parameters and stimulus set are familiar.
Based on these observations a series of key task parameters were identified (e.g., introductions, seeking advice and help, setting out a treatment plan).
The task of setting optimal parameters for a testing process is often difficult in practical situations due to uncertainties in both incoming product yield and testing equipment condition during the testing process.
All task parameter sets are described as follows: active/inactive lever switch schedule, active lever response schedule, OLP response schedule (observing light duration), i.e., for the final training session, S30, the schedule was FT90s, VR10-20, OLP FR1 (15s).–20, OLP FR1 (15s).
As Figure 11 shows, we can choose the task and their modes in the most left red box, set the task's parameters through the popup dialog box, and then submit the task by Submit Job button.
One of the main advantages of the new method is that setting its parameters is an intuitive task to the decision maker.
For this reason, a new parameter selection scheme for simulated annealing is proposed that sets task mapping specific optimization parameters automatically.
